{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listed Volatility and Variance Derivatives\n",
    "\n",
    "**Dr. Yves J. Hilpisch &mdash; Wiley Finance (2016)**\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/lvvd_cover.png\" alt=\"Derivatives Analytics with Python\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VSTOXX Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collecting Option Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('scripts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path = 'data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import index_collect_option_data as icod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20161020'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today = dt.datetime.now()\n",
    "## make sure to hit a business day\n",
    "target_day = today - dt.timedelta(days=1)\n",
    "ds = target_day.strftime('%Y%m%d')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## adjust maturity parameters if necessary\n",
    "call_data = icod.get_data_from_www(oType='Call', matMonth=3,\n",
    "                                   matYear=2017, date=ds)\n",
    "put_data = icod.get_data_from_www(oType='Put', matMonth=3,\n",
    "                                  matYear=2017, date=ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## parse the raw data\n",
    "calls = icod.parse_data(call_data, target_day)\n",
    "puts = icod.parse_data(put_data, target_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 75 entries, (2016-10-20 00:00:00, 500.0) to (2016-10-20 00:00:00, 6000.0)\n",
      "Data columns (total 8 columns):\n",
      "Version number          75 non-null float64\n",
      "Opening price           75 non-null float64\n",
      "Daily high              75 non-null float64\n",
      "Daily low               75 non-null float64\n",
      "Last price              75 non-null float64\n",
      "Daily settlem. price    75 non-null float64\n",
      "Traded contracts        75 non-null int64\n",
      "Open interest           75 non-null int64\n",
      "dtypes: float64(6), int64(2)\n",
      "memory usage: 5.3+ KB\n"
     ]
    }
   ],
   "source": [
    "calls.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 75 entries, (2016-10-20 00:00:00, 500.0) to (2016-10-20 00:00:00, 6000.0)\n",
      "Data columns (total 8 columns):\n",
      "Version number          75 non-null float64\n",
      "Opening price           75 non-null float64\n",
      "Daily high              75 non-null float64\n",
      "Daily low               75 non-null float64\n",
      "Last price              75 non-null float64\n",
      "Daily settlem. price    75 non-null float64\n",
      "Traded contracts        75 non-null int64\n",
      "Open interest           75 non-null int64\n",
      "dtypes: float64(6), int64(2)\n",
      "memory usage: 5.3+ KB\n"
     ]
    }
   ],
   "source": [
    "puts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "calls = pd.DataFrame(calls.rename(\n",
    "        columns={'Daily settlem. price': 'Call_Price'}\n",
    "                            ).pop('Call_Price').astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "puts = pd.DataFrame(puts.rename(\n",
    "        columns={'Daily settlem. price': 'Put_Price'}\n",
    "                            ).pop('Put_Price').astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 63 entries, (2016-10-20 00:00:00, 1400.0) to (2016-10-20 00:00:00, 3900.0)\n",
      "Data columns (total 2 columns):\n",
      "Call_Price    63 non-null float64\n",
      "Put_Price     63 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset = icod.merge_and_filter(puts, calls)\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 63 entries, (2016-10-20 00:00:00, 1400.0) to (2016-10-20 00:00:00, 3900.0)\n",
      "Data columns (total 2 columns):\n",
      "Call_Price    63 non-null float64\n",
      "Put_Price     63 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "os = icod.collect_option_series(3, 2017, target_day)\n",
    "os.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# uncomment to initiate the process (takes a few minutes)\n",
    "# %time icod.data_collection(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: data/index_option_series.h5\n",
       "/Dec16            frame        (shape->[1098,2])\n",
       "/Nov16            frame        (shape->[849,2]) \n",
       "/Oct16            frame        (shape->[426,2]) "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store = pd.HDFStore(path + 'index_option_series.h5', 'r')\n",
    "store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Dec16 = store['Dec16']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 1098 entries, (2016-09-23 00:00:00, 1800.0) to (2016-10-20 00:00:00, 3500.0)\n",
      "Data columns (total 2 columns):\n",
      "Call_Price    1098 non-null float64\n",
      "Put_Price     1098 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 25.7+ KB\n"
     ]
    }
   ],
   "source": [
    "Dec16.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "store.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Call_Price</th>\n",
       "      <th>Put_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pricing day</th>\n",
       "      <th>Strike price</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">2016-09-23</th>\n",
       "      <th>2700.0</th>\n",
       "      <td>348.6</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2725.0</th>\n",
       "      <td>326.8</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2750.0</th>\n",
       "      <td>305.5</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2775.0</th>\n",
       "      <td>284.5</td>\n",
       "      <td>40.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2800.0</th>\n",
       "      <td>264.0</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2825.0</th>\n",
       "      <td>243.9</td>\n",
       "      <td>49.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850.0</th>\n",
       "      <td>224.3</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875.0</th>\n",
       "      <td>205.3</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900.0</th>\n",
       "      <td>186.9</td>\n",
       "      <td>67.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925.0</th>\n",
       "      <td>169.2</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Call_Price  Put_Price\n",
       " Pricing day Strike price                       \n",
       "2016-09-23   2700.0             348.6       29.3\n",
       "             2725.0             326.8       32.6\n",
       "             2750.0             305.5       36.2\n",
       "             2775.0             284.5       40.3\n",
       "             2800.0             264.0       44.8\n",
       "             2825.0             243.9       49.7\n",
       "             2850.0             224.3       55.2\n",
       "             2875.0             205.3       61.2\n",
       "             2900.0             186.9       67.8\n",
       "             2925.0             169.2       75.1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dec16.ix[25:35]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the Sub-Indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Expiry V6I1</th>\n",
       "      <th>Expiry V6I2</th>\n",
       "      <th>Expiry V6I3</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>V6I2</th>\n",
       "      <th>V6I3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.759804</td>\n",
       "      <td>22.360188</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.299727</td>\n",
       "      <td>22.740036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.613843</td>\n",
       "      <td>22.675634</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-02</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.904232</td>\n",
       "      <td>22.070847</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.487130</td>\n",
       "      <td>22.050495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.139754</td>\n",
       "      <td>22.280183</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.743333</td>\n",
       "      <td>21.809108</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-08</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.508476</td>\n",
       "      <td>23.306916</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.073811</td>\n",
       "      <td>24.015911</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.199642</td>\n",
       "      <td>24.363648</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.009313</td>\n",
       "      <td>26.307574</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.585681</td>\n",
       "      <td>30.253168</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.680603</td>\n",
       "      <td>27.372170</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.116152</td>\n",
       "      <td>25.510208</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>2016-02-19 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.524591</td>\n",
       "      <td>23.509207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>2016-02-19 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.757165</td>\n",
       "      <td>24.740060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.616427</td>\n",
       "      <td>24.665801</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.908727</td>\n",
       "      <td>23.629867</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.398907</td>\n",
       "      <td>21.488879</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.839390</td>\n",
       "      <td>21.677283</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Expiry V6I1 Expiry V6I2          Expiry V6I3       V6I1       V6I2  \\\n",
       "2015-11-27  2015-12-18  2016-01-15                  NaN  23.759804  22.360188   \n",
       "2015-11-30  2015-12-18  2016-01-15                  NaN  25.299727  22.740036   \n",
       "2015-12-01  2015-12-18  2016-01-15                  NaN  25.613843  22.675634   \n",
       "2015-12-02  2015-12-18  2016-01-15                  NaN  24.904232  22.070847   \n",
       "2015-12-03  2015-12-18  2016-01-15                  NaN  24.487130  22.050495   \n",
       "2015-12-04  2015-12-18  2016-01-15                  NaN  24.139754  22.280183   \n",
       "2015-12-07  2015-12-18  2016-01-15                  NaN  23.743333  21.809108   \n",
       "2015-12-08  2015-12-18  2016-01-15                  NaN  26.508476  23.306916   \n",
       "2015-12-09  2015-12-18  2016-01-15                  NaN  28.073811  24.015911   \n",
       "2015-12-10  2015-12-18  2016-01-15                  NaN  28.199642  24.363648   \n",
       "2015-12-11  2015-12-18  2016-01-15                  NaN  32.009313  26.307574   \n",
       "2015-12-14  2015-12-18  2016-01-15                  NaN  43.585681  30.253168   \n",
       "2015-12-15  2015-12-18  2016-01-15                  NaN  41.680603  27.372170   \n",
       "2015-12-16  2015-12-18  2016-01-15                  NaN  49.116152  25.510208   \n",
       "2015-12-17         NaT  2016-01-15  2016-02-19 00:00:00        NaN  22.524591   \n",
       "2015-12-18         NaT  2016-01-15  2016-02-19 00:00:00        NaN  23.757165   \n",
       "2015-12-21  2016-01-15  2016-02-19                  NaN  23.616427  24.665801   \n",
       "2015-12-22  2016-01-15  2016-02-19                  NaN  21.908727  23.629867   \n",
       "2015-12-23  2016-01-15  2016-02-19                  NaN  19.398907  21.488879   \n",
       "2015-12-24  2016-01-15  2016-02-19                  NaN  19.839390  21.677283   \n",
       "\n",
       "                 V6I3  \n",
       "2015-11-27        NaN  \n",
       "2015-11-30        NaN  \n",
       "2015-12-01        NaN  \n",
       "2015-12-02        NaN  \n",
       "2015-12-03        NaN  \n",
       "2015-12-04        NaN  \n",
       "2015-12-07        NaN  \n",
       "2015-12-08        NaN  \n",
       "2015-12-09        NaN  \n",
       "2015-12-10        NaN  \n",
       "2015-12-11        NaN  \n",
       "2015-12-14        NaN  \n",
       "2015-12-15        NaN  \n",
       "2015-12-16        NaN  \n",
       "2015-12-17  23.509207  \n",
       "2015-12-18  24.740060  \n",
       "2015-12-21        NaN  \n",
       "2015-12-22        NaN  \n",
       "2015-12-23        NaN  \n",
       "2015-12-24        NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import index_subindex_calculation as isc\n",
    "si = isc.make_subindex(path)\n",
    "si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-11-27', '2015-11-30', '2015-12-01', '2015-12-02',\n",
       "               '2015-12-03', '2015-12-04', '2015-12-07', '2015-12-08',\n",
       "               '2015-12-09', '2015-12-10', '2015-12-11', '2015-12-14',\n",
       "               '2015-12-15', '2015-12-16', '2015-12-17', '2015-12-18',\n",
       "               '2015-12-21', '2015-12-22', '2015-12-23', '2015-12-24'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4357 entries, 1999-01-04 to 2016-02-12\n",
      "Data columns (total 9 columns):\n",
      "V2TX    4357 non-null float64\n",
      "V6I1    3906 non-null float64\n",
      "V6I2    4357 non-null float64\n",
      "V6I3    4296 non-null float64\n",
      "V6I4    4357 non-null float64\n",
      "V6I5    4357 non-null float64\n",
      "V6I6    4340 non-null float64\n",
      "V6I7    4357 non-null float64\n",
      "V6I8    4343 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 340.4 KB\n"
     ]
    }
   ],
   "source": [
    "vs_url = 'https://www.stoxx.com/document/'\n",
    "vs_url += 'Indices/Current/HistoricalData/h_vstoxx.txt'\n",
    "vs = pd.read_csv(vs_url,  # filename\n",
    "                 index_col=0,  # index column (dates)\n",
    "                 parse_dates=True,  # parse date information\n",
    "                 dayfirst=True, # day before month\n",
    "                 header=2)  # header/column names\n",
    "vs.to_csv(path + 'vs.csv')  # write as CSV file\n",
    "# vs = vs[vs.index <= '2015-12-31']\n",
    "vs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALC</th>\n",
       "      <th>REAL</th>\n",
       "      <th>DIFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>22.360188</td>\n",
       "      <td>22.3778</td>\n",
       "      <td>-0.017612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>22.740036</td>\n",
       "      <td>22.7497</td>\n",
       "      <td>-0.009664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>22.675634</td>\n",
       "      <td>22.7239</td>\n",
       "      <td>-0.048266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-02</th>\n",
       "      <td>22.070847</td>\n",
       "      <td>22.0465</td>\n",
       "      <td>0.024347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03</th>\n",
       "      <td>22.050495</td>\n",
       "      <td>22.0724</td>\n",
       "      <td>-0.021905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04</th>\n",
       "      <td>22.280183</td>\n",
       "      <td>22.3454</td>\n",
       "      <td>-0.065217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07</th>\n",
       "      <td>21.809108</td>\n",
       "      <td>21.8242</td>\n",
       "      <td>-0.015092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-08</th>\n",
       "      <td>23.306916</td>\n",
       "      <td>23.2577</td>\n",
       "      <td>0.049216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>24.015911</td>\n",
       "      <td>23.9786</td>\n",
       "      <td>0.037311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>24.363648</td>\n",
       "      <td>24.3061</td>\n",
       "      <td>0.057548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>26.307574</td>\n",
       "      <td>26.3378</td>\n",
       "      <td>-0.030226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>30.253168</td>\n",
       "      <td>30.2815</td>\n",
       "      <td>-0.028332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>27.372170</td>\n",
       "      <td>27.4133</td>\n",
       "      <td>-0.041130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>25.510208</td>\n",
       "      <td>25.4394</td>\n",
       "      <td>0.070808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>22.524591</td>\n",
       "      <td>22.5024</td>\n",
       "      <td>0.022191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>23.757165</td>\n",
       "      <td>23.7604</td>\n",
       "      <td>-0.003235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>24.665801</td>\n",
       "      <td>24.6237</td>\n",
       "      <td>0.042101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>23.629867</td>\n",
       "      <td>23.6439</td>\n",
       "      <td>-0.014033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>21.488879</td>\n",
       "      <td>21.4523</td>\n",
       "      <td>0.036579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 CALC     REAL      DIFF\n",
       "2015-11-27  22.360188  22.3778 -0.017612\n",
       "2015-11-30  22.740036  22.7497 -0.009664\n",
       "2015-12-01  22.675634  22.7239 -0.048266\n",
       "2015-12-02  22.070847  22.0465  0.024347\n",
       "2015-12-03  22.050495  22.0724 -0.021905\n",
       "2015-12-04  22.280183  22.3454 -0.065217\n",
       "2015-12-07  21.809108  21.8242 -0.015092\n",
       "2015-12-08  23.306916  23.2577  0.049216\n",
       "2015-12-09  24.015911  23.9786  0.037311\n",
       "2015-12-10  24.363648  24.3061  0.057548\n",
       "2015-12-11  26.307574  26.3378 -0.030226\n",
       "2015-12-14  30.253168  30.2815 -0.028332\n",
       "2015-12-15  27.372170  27.4133 -0.041130\n",
       "2015-12-16  25.510208  25.4394  0.070808\n",
       "2015-12-17  22.524591  22.5024  0.022191\n",
       "2015-12-18  23.757165  23.7604 -0.003235\n",
       "2015-12-21  24.665801  24.6237  0.042101\n",
       "2015-12-22  23.629867  23.6439 -0.014033\n",
       "2015-12-23  21.488879  21.4523  0.036579"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp = pd.concat((si['V6I2'], vs['V6I2']),\n",
    "          axis=1, join='inner')\n",
    "comp.index = comp.index.normalize()\n",
    "comp.columns = ['CALC', 'REAL']\n",
    "comp['DIFF'] = comp['CALC'] - comp['REAL']\n",
    "comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oyDrLjRgRHrWaMEGjVhIfFKxERH63a5fBW285qVEjRJcu2ucfC1q2DNK0aZB5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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a66b2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns; sns.set()\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.family'] = 'serif'  # set serif font\n",
    "%matplotlib inline\n",
    "comp[['CALC', 'REAL']].plot(style=['ro', 'b'], figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8jIVLls9p0Js3b2PTpq37X7AhNm/e1vPc/VgnwJIlD5nT+qp1bv+x9c91nLPH2I919sPE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\nGenDPbRFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cbc0b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 6));\n",
    "plt.bar(comp.index, comp['DIFF']);\n",
    "plt.gcf().autofmt_xdate();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the VSTOXX Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 839 ms, sys: 10.7 ms, total: 850 ms\n",
      "Wall time: 854 ms\n"
     ]
    }
   ],
   "source": [
    "import index_vstoxx_calculation as ivc\n",
    "%time data = ivc.calculate_vstoxx(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4357 entries, 1999-01-04 to 2016-02-12\n",
      "Data columns (total 20 columns):\n",
      "V2TX                 4357 non-null float64\n",
      "V6I1                 3906 non-null float64\n",
      "V6I2                 4357 non-null float64\n",
      "V6I3                 4296 non-null float64\n",
      "V6I4                 4357 non-null float64\n",
      "V6I5                 4357 non-null float64\n",
      "V6I6                 4340 non-null float64\n",
      "V6I7                 4357 non-null float64\n",
      "V6I8                 4343 non-null float64\n",
      "Settlement date 1    4357 non-null datetime64[ns]\n",
      "Settlement date 2    4357 non-null datetime64[ns]\n",
      "Life time 1          4357 non-null int64\n",
      "Life time 2          4357 non-null int64\n",
      "Use V6I2             4357 non-null bool\n",
      "Subindex to use 1    4357 non-null float64\n",
      "Subindex to use 2    4357 non-null float64\n",
      "Part 1               4357 non-null float64\n",
      "Part 2               4357 non-null float64\n",
      "VSTOXX               4357 non-null float64\n",
      "Difference           4357 non-null float64\n",
      "dtypes: bool(1), datetime64[ns](2), float64(15), int64(2)\n",
      "memory usage: 685.0 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0012799470095575995"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## output: average error of re-calculation\n",
    "data['Difference'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eRcf6ANha9HpL0XuiHODmMKD8B+TGjYOPVJddJm84zAc0nr8qO1s+j1aORLzg9Zpgsai1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\nQ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G9VW7pijHUFwAEcBdAHwHYAJ6JaqCjAGEsGMAzAaQDPAygD8CRjrBnnXKoqbQLUL1K/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb5f050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## original vs. re-calculated VSTOXX index\n",
    "data[['V2TX', 'VSTOXX']].plot(subplots=True, figsize=(10, 6),\n",
    "                              style=\"blue\", grid=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fHbIcNl92lauZ/XV4vTzQetq99joxy2D1/EbXfuAhsS04cR4deyZuVAif8nHa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\nZm1Jd1lr1w6N91tJG8i7GvELa+3fUy4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d8f4210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## differences between single values\n",
    "data['Difference'].plot(figsize=(10, 6), style=\"r\", grid=True,\n",
    "                        ylim=(-1, 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Scripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### index_collect_option_data.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### index_subindex_calculation.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### index_vstoxx_calculation.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:team@tpq.io\">team@tpq.io</a>\n",
    "\n",
    "**DX Analytics** |\n",
    "<a href=\"http://dx-analytics.com\">http://dx-analytics.com</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance Books** |\n",
    "<a href=\"http://books.tpq.io\" target=\"_blank\">http://books.tpq.io</a>\n",
    "\n",
    "**Python for Finance Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">http://training.tpq.io</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
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}
